Identification of sunflower seeds with deep convolutional neural networks
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Date
2020-10-13
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Springer
Abstract
In the food and agricultural industries, it is crucial to identify and to choose correct sunflower seeds that meet specific requirements. Deep learning and computer vision methods can help identify sunflower seeds. In this study, a computer vision system was proposed, trained, and tested to identify four varieties of sunflower seeds using deep learning methodology and a regular color camera. Image acquisition was carried out under controlled illumination conditions. An image segmentation procedure was employed to reduce the workload in obtaining training images required for training deep convolutional neural network models. Three deep learning architectures, namely AlexNet, GoogleNet, and ResNet, were investigated for identifying sunflower seeds in this study. Different solver types were also evaluated to determine the best deep learning model in terms of both accuracy and training time. About 4800 sunflower seeds were inspected individually for training and testing. The highest classification accuracy (95%) was succeeded with the GoogleNet algorithm.
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Keywords
Food science & technology, Sunflower, Seed classification, Deep learning, Neural networks, Computer vision, Vision, Image, Machine, System, Agricultural robots, Agriculture, Computer vision, Convolution, Convolutional neural networks, Deep neural networks, Image segmentation, Learning systems, Agricultural industries, Classification accuracy, Computer vision system, Illumination conditions, Learning architectures, Learning architectures, Learning models, Segmentation procedure, Training and testing, Deep learning
Citation
Kurtulmuş, F. (2020). "Identification of sunflower seeds with deep convolutional neural networks". Journal of Food Measurement and Characterization, 15(2), 1024-1033.